摘要
基于稀疏表示的人脸鉴别方法通过提高字典的判别力来提高识别准确率,本文针对小样本训练,提出一种新的融合字典学习方法.首先利用Fisher判别准则及LBP金字塔进行数据预处理;其次提出新的融合字典学习模型,该模型由公共字典、类别特色字典及扰动字典三部分构成,分别提取数据共性、不同类别数据的特殊性以及异常情况下的数据扰动性;最后根据融合字典模型提出一种新的分类器,并在AR、YALE、CMU-PIE、LFW人脸数据库进行实验,结果表明本文算法具有更高的识别率和有效性.
Face recognition based on sparse representation improves recognition accuracy by improving the discriminative power of dictionaries.This paper proposes a fusion dictionary learning algorithm.Firstly,Fisher criterion and LBP pyramid are used to preprocess data.Then,based on the features of the data,a fusion dictionary learning model is proposed to comprehensively study the common dictionary,class-specific dictionary,and perturbation dictionary to extract the commonness between the data,the particularity of the different types of data,and the data perturbation under abnormal conditions.Finally,a new classifier is proposed based on the model,and experiments are performed on face databases such as AR,YALE,CMU-PIE,LFW.The results show that this algorithm has higher recognition rate and effectiveness.
作者
狄岚
矫慧文
梁久祯
DI Lan;JIAO Hui-wen;LIANG Jiu-zhen(School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,China;Key Laboratory of Ministry of Public Security for Road Traffic Safety,Wuxi 214151,China;School of Information Science and Engineering,Changzhou University,Changzhou 213164,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2021年第1期154-160,共7页
Journal of Chinese Computer Systems
基金
江苏省研究生科研与实践创新计划项目(KYCX19_1895)资助
道路交通安全公安部重点实验室开放课题基金项目(2020ZDSYSKFKT03-2)资助。
